Iris segmentation using game theory

Robust segmentation of an iris image plays an important role in iris recognition. However, the nonlinear deformations, pupil dilations, head rotations, motion blurs, reflections, nonuniform intensities, low image contrast, camera angles and diffusions, and presence of eyelids and eyelashes often hamper the conventional iris/pupil localization methods, which utilize the region-based or the gradient-based boundary-finding information. The novelty of this research effort is that we describe a new iris segmentation scheme using game theory to elicit iris/pupil boundaries from a nonideal iris image. We apply a parallel game-theoretic decision making procedure by modifying Chakraborty and Duncan's algorithm, which integrates (1) the region-based segmentation and gradient-based boundary-finding methods and (2) fuses the complementary strengths of each of these individual methods. This integrated scheme forms a unified approach, which is robust to noise and poor localization, and less affected by weak iris/sclera boundaries. The verification and identification performance of the proposed method are validated using the ICE 2005, the UBIRIS Version 1, WVU Nonideal, and the CASIA Version 3 data sets.